import keras
import numpy as np
import more_itertools
import json
from neo4j.v1 import GraphDatabase, Driver
class GraphSequence(keras.utils.Sequence):
def __init__(self, args, batch_size=32, test=False):
self.batch_size = batch_size
self.query = """
MATCH p=
(person:PERSON)
-[:WROTE]->
(review:REVIEW {dataset_name:{dataset_name}, test:{test}})
-[:OF]->
(product:PRODUCT)
RETURN person.style_preference + product.style as x, review.score as y
"""
self.query_params = {
"dataset_name": "article_0",
"test": test
}
with open('./settings.json') as f:
self.settings = json.load(f)[args.database]
driver = GraphDatabase.driver(
self.settings["neo4j_url"],
auth=(self.settings["neo4j_user"], self.settings["neo4j_password"]))
with driver.session() as session:
data = session.run(self.query, **self.query_params).data()
data = [ (np.array(i["x"]), i["y"]) for i in data]
# Split the data up into "batches"
data = more_itertools.chunked(data, self.batch_size)
# Format our batches in the way Keras expects them:
# An array of tuples (x_batch, y_batch)
# An x_batch is a numpy array of shape (batch_size, 12),
# containing the concatenated style and style_preference vectors.
# A y_batch is a numpy array of shape (batch_size,1) containing the review scores.
self.data = [ (np.array([j[0] for j in i]), np.array([j[1] for j in i])) for i in data]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
